The similarity between object and image of negative asymmetrical holographic lenses (HLs) stored in a low-toxicity photopolymer has been evaluated theoretically and experimentally. Asymmetrical experimental setups with negative focal lengths have been used to obtain HLs. For this purpose, the resolution of the HLs was calculated using the convolution theorem. A USAF 1951 test was used as an object and the impulse responses of the HLs, which in this case was the amplitude spread function (ASF), were obtained with two different methods: using a CCD sensor and a Hartmann Shack (HS) wavefront sensor. For a negative asymmetrically recorded HL a maximum resolution of 11.31 lp/mm was obtained. It was evaluated at 473 nm wavelength. A theoretical study of object-image similarity had carried out using the MSE (mean squared error) metric to evaluate the experimental results obtained quantitatively.
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http://dx.doi.org/10.3390/polym14245426 | DOI Listing |
Sci Rep
January 2025
Department of Computer Science and Information Technology, Benazir Bhutto Shaheed University Lyari, Karachi, 75660, Pakistan.
Deep learning-based medical image analysis has shown strong potential in disease categorization, segmentation, detection, and even prediction. However, in high-stakes and complex domains like healthcare, the opaque nature of these models makes it challenging to trust predictions, particularly in uncertain cases. This sort of uncertainty can be crucial in medical image analysis; diabetic retinopathy is an example where even slight errors without an indication of confidence can have adverse impacts.
View Article and Find Full Text PDFSensors (Basel)
December 2024
School of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.
Electrocardiogram (ECG) signals contain complex and diverse features, serving as a crucial basis for arrhythmia diagnosis. The subtle differences in characteristics among various types of arrhythmias, coupled with class imbalance issues in datasets, often hinder existing models from effectively capturing key information within these complex signals, leading to a bias towards normal classes. To address these challenges, this paper proposes a method for arrhythmia classification based on a multi-branch, multi-head attention temporal convolutional network (MB-MHA-TCN).
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January 2025
Department of Electronics, Information and Communication Engineering, Kangwon National University, Samcheok, Republic of Korea.
Detecting brain tumours (BT) early improves treatment possibilities and increases patient survival rates. Magnetic resonance imaging (MRI) scanning offers more comprehensive information, such as better contrast and clarity, than any alternative scanning process. Manually separating BTs from several MRI images gathered in medical practice for cancer analysis is challenging and time-consuming.
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December 2024
School of Mechanical and Electrical Engineering, Qiqihar University, Qiqihar, 161006, China.
A prediction model of the pig house environment based on Bayesian optimization (BO), squeeze and excitation block (SE), convolutional neural network (CNN) and gated recurrent unit (GRU) is proposed to improve the prediction accuracy and animal welfare and take control measures in advance. To ensure the optimal model configuration, the model uses a BO algorithm to fine-tune hyper-parameters, such as the number of GRUs, initial learning rate and L2 normal form regularization factor. The environmental data are fed into the SE-CNN block, which extracts the local features of the data through convolutional operations.
View Article and Find Full Text PDFHeliyon
October 2024
Department of Basic Sciences, College of Science, and Theoretical Studies, Saudi Electronic University, Riyadh, 11673, Saudi Arabia.
In this article, by applying the convolution principle and symmetric -calculus, we develop a new generalized symmetric -difference operator of convolution type, which is applicable in the domain . Utilizing this operator, we construct, analyze, and evaluate two new sets of meromorphically harmonic functions in the Janowski domain. Furthermore, we investigate the convolution properties and necessary conditions for a function to belong to the class , examining the sufficiency conditions for to satisfy these properties.
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